Guo Zheng, Wang Lei, Li Yongjin, Gong Xue, Yao Chen, Ma Wencai, Wang Dong, Li Yanhui, Zhu Jing, Zhang Min, Yang Da, Rao Shaoqi, Wang Jing
Department of Bioinformatics, Bio-pharmaceutical Key Laboratory of Heilongjiang Province-Incubator of State Key Laboratory, Harbin Medical University, Harbin 150086, China.
Bioinformatics. 2007 Aug 15;23(16):2121-8. doi: 10.1093/bioinformatics/btm294. Epub 2007 Jun 1.
Current high-throughput protein-protein interaction (PPI) data do not provide information about the condition(s) under which the interactions occur. Thus, the identification of condition-responsive PPI sub-networks is of great importance for investigating how a living cell adapts to changing environments.
In this article, we propose a novel edge-based scoring and searching approach to extract a PPI sub-network responsive to conditions related to some investigated gene expression profiles. Using this approach, what we constructed is a sub-network connected by the selected edges (interactions), instead of only a set of vertices (proteins) as in previous works. Furthermore, we suggest a systematic approach to evaluate the biological relevance of the identified responsive sub-network by its ability of capturing condition-relevant functional modules. We apply the proposed method to analyze a human prostate cancer dataset and a yeast cell cycle dataset. The results demonstrate that the edge-based method is able to efficiently capture relevant protein interaction behaviors under the investigated conditions.
Supplementary data are available at Bioinformatics online.
当前的高通量蛋白质-蛋白质相互作用（PPI）数据并未提供相互作用发生时所处条件的信息。因此，识别条件响应性PPI子网对于研究活细胞如何适应不断变化的环境至关重要。
在本文中，我们提出了一种新颖的基于边的评分和搜索方法，以提取对与某些研究的基因表达谱相关的条件有响应的PPI子网。使用这种方法，我们构建的是一个由选定边（相互作用）连接的子网，而不是像以前的工作那样仅仅是一组顶点（蛋白质）。此外，我们提出了一种系统方法，通过其捕获与条件相关的功能模块的能力来评估所识别的响应性子网的生物学相关性。我们将所提出的方法应用于分析人类前列腺癌数据集和酵母细胞周期数据集。结果表明，基于边的方法能够在研究条件下有效地捕获相关的蛋白质相互作用行为。
补充数据可在《生物信息学》在线获取。
